ML Stock Forecasting Report

📋 Trading Strategy Overview

This analysis uses a daily-signal threshold strategy. Each model produces one prediction per trading day — an expected log-return for the next day. The trading rules are:

BUY — if the predicted return exceeds the signal threshold (+0.2%), the model signals to enter a long position at market open and close it at the end of the day.
HOLD / CASH — if the predicted return is below the threshold, the model stays out of the market (no position).
Maximum 1 trade per day — the model generates exactly one signal per trading day. There is no intraday re-entry or multiple transactions.

Coverage in the metrics table shows what fraction of days the model actually traded (signal exceeded threshold). The remaining days the model sat in cash, avoiding uncertain moves.

🎯 Next Day Trading Recommendation

Action: BUY

Expected Return: 1.92%

Raw DA: 85.2% (Buy & Hold baseline: 53.0%)

📊 Next Day Price Predictions & Recommendations

📊 Executive Summary

This comprehensive ML analysis of AAPL achieves a best raw directional accuracy of 85.2% (vs Buy & Hold baseline of 53.0%).

📈 Model Performance Metrics

Model	RMSE	MAE	Raw DA	Confident DA	Coverage	Trades
Buy & Hold (baseline)	—	—	53.0%	—	100.0%	—
LINEAR	0.0124	0.0091	76.5%	77.5%	87.7%	213 / 243
RF	0.0125	0.0077	82.3%	87.6%	83.1%	202 / 243
XGB	0.0121	0.0072	85.2%	90.5%	82.7%	201 / 243
GBR	0.0115	0.0071	84.8%	90.3%	84.8%	206 / 243
ELASTICNET	0.0151	0.0102	72.0%	79.0%	74.5%	181 / 243
EXTRATREES	0.0124	0.0079	84.4%	86.8%	84.0%	204 / 243
SGD	0.0131	0.0089	79.0%	81.0%	88.9%	216 / 243

📖 Metric Descriptions

RMSE (Root Mean Squared Error) — average magnitude of prediction errors, penalizing larger errors more heavily. Lower is better.
MAE (Mean Absolute Error) — average absolute difference between predicted and actual log-returns. Lower is better.
Raw DA (Raw Directional Accuracy) — percentage of days where the model correctly predicted the direction of price movement (up/down), calculated on all trading days without any filtering.
Confident DA (High-Confidence Directional Accuracy) — directional accuracy calculated only on days where the model's predicted return exceeded the signal threshold (±0.2%). Represents the trading strategy accuracy — the model trades only when confident.
Coverage — fraction of trading days where the model generates a trading signal (|prediction| > threshold). Higher coverage = more frequent trading.
Trades — absolute number of days the model traded out of total test days (e.g. "228 / 251" means the model traded on 228 out of 251 available days).
Buy & Hold (baseline) — directional accuracy of a naive strategy that always predicts "price goes up". Equals the percentage of days the market actually rose. Models should exceed this to demonstrate real predictive power.

📊 Model Comparison & Error Analysis

📊 Strategy Performance Analysis

📊 Prediction Stability & Agreement

📊 Feature Analysis & Correlations

🎯 Conclusions & Recommendations

Model Performance: ML models achieve raw directional accuracies from 72.0% to 85.2% (Buy & Hold baseline: 53.0%)
Risk Management: Threshold-based strategy (0.2% threshold) effectively reduces false signals and improves signal quality
Implementation: Consider implementing the recommended trading strategy with proper position sizing and risk management protocols
Monitoring: Regular model retraining and performance monitoring is essential for maintaining predictive accuracy
Next Steps: Focus on the best performing model (XGBoost) for production deployment with continuous validation

⚠️ Disclaimer

This analysis is for informational purposes only and should not be considered as financial advice. Past performance does not guarantee future results. Always conduct your own research and consult with qualified financial advisors before making investment decisions.

🤖 ML Stock Forecasting Report

AAPL Analysis